2020/09/30 by Neng Wan, Aditya Gahlawat, Wan, Neng +7 · 2 citations
Computer Science · Engineering · Mathematics · #Action (physics) #Applied mathematics #Artificial intelligence #Computer science #Control (management) #Control theory (sociology) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Mathematical optimization #Mathematics #Monte Carlo method #Multiagent Systems (cs.MA) #Optimal control #Optimization and Control (math.OC) #Path (computing) #Path integral formulation #Robotics (cs.RO) #Stochastic control #Stochastic differential equation #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #UAV Applications and Optimization #cs.LG #cs.MA #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.2009.14775
published in arXiv (Cornell University) (Cornell University) · To appear in American Control Conference 2021, New Orleans, LA, USA
openalex publication_date 2020/09/30 · arxiv created 2021/03/21 · arxiv updated 2021/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and neighboring agents. Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems. When solving for the local control actions, the joint optimality equation for each subsystem is cast as a linear partial differential equation and solved using the Feynman-Kac formula. The solution and the optimal control action are then formulated as path integrals and approximated by a Monte-Carlo method. Numerical verification is provided through a simulation example consisting of a team of cooperative UAVs.